Deeplearning4j is an open-source, distributed deep-learning library written for Java and Scala. Integrated with Hadoop and Spark, DL4J is designed to be used in business environments on distributed GPUs and CPUs.
Word2Vec is a method of computing vector representations of words introduced by a team of researchers at Google led by Tomas Mikolov.
Audience
This course is directed at researchers, engineers and developers seeking to utilize Deeplearning4J to construct Word2Vec models.
Getting Started
DL4J Examples in a Few Easy Steps
Using DL4J In Your Own Projects: Configuring the POM.xml File
Word2Vec
Introduction
Neural Word Embeddings
Amusing Word2vec Results
the Code
Anatomy of Word2Vec
Setup, Load and Train
A Code Example
Troubleshooting & Tuning Word2Vec
Word2vec Use Cases
Foreign Languages
GloVe (Global Vectors) & Doc2Vec

Caffe is a deep learning framework made with expression, speed, and modularity in mind.
This course explores the application of Caffe as a Deep learning framework for image recognition using MNIST as an example
Audience
This course is suitable for Deep Learning researchers and engineers interested in utilizing Caffe as a framework.
After completing this course, delegates will be able to:
understand Caffe’s structure and deployment mechanisms
carry out installation / production environment / architecture tasks and configuration
assess code quality, perform debugging, monitoring
implement advanced production like training models, implementing layers and logging
Installation
Docker
Ubuntu
RHEL / CentOS / Fedora installation
Windows
Caffe Overview
Nets, Layers, and Blobs: the anatomy of a Caffe model.
Forward / Backward: the essential computations of layered compositional models.
Loss: the task to be learned is defined by the loss.
Solver: the solver coordinates model optimization.
Layer Catalogue: the layer is the fundamental unit of modeling and computation – Caffe’s catalogue includes layers for state-of-the-art models.
Interfaces: command line, Python, and MATLAB Caffe.
Data: how to caffeinate data for model input.
Caffeinated Convolution: how Caffe computes convolutions.
New models and new code
Detection with Fast R-CNN
Sequences with LSTMs and Vision + Language with LRCN
Pixelwise prediction with FCNs
Framework design and future
Examples:
MNIST

TensorFlow is a 2nd Generation API of Google's open source software library for Deep Learning. The system is designed to facilitate research in machine learning, and to make it quick and easy to transition from research prototype to production system.
Audience
This course is intended for engineers seeking to use TensorFlow for their Deep Learning projects
After completing this course, delegates will:
understand TensorFlow’s structure and deployment mechanisms
be able to carry out installation / production environment / architecture tasks and configuration
be able to assess code quality, perform debugging, monitoring
be able to implement advanced production like training models, building graphs and logging
Machine Learning and Recursive Neural Networks (RNN) basics
NN and RNN
Backprogation
Long short-term memory (LSTM)
TensorFlow Basics
Creation, Initializing, Saving, and Restoring TensorFlow variables
Feeding, Reading and Preloading TensorFlow Data
How to use TensorFlow infrastructure to train models at scale
Visualizing and Evaluating models with TensorBoard
TensorFlow Mechanics 101
Prepare the Data
Download
Inputs and Placeholders
Build the Graph
Inference
Loss
Training
Train the Model
The Graph
The Session
Train Loop
Evaluate the Model
Build the Eval Graph
Eval Output
Advanced Usage
Threading and Queues
Distributed TensorFlow
Writing Documentation and Sharing your Model
Customizing Data Readers
Using GPUs
Manipulating TensorFlow Model Files
TensorFlow Serving
Introduction
Basic Serving Tutorial
Advanced Serving Tutorial
Serving Inception Model Tutorial

NobleProg® Limited 2004 - 2016 All Rights Reserved
This site is operated by the NobleProg franchisor . If you interested in opening a franchise in your country, please visit http://training-franchise.com for more information.

NobleProg® is a registered trade mark of NobleProg Limited and/or its affiliates.